T A Mohanaprakash, Ranganayaki V.C, M. S. Minu, D. A. · 5 authors
Many have long aimed to create a safe electronic voting system that maintains the confidentiality and integrity of traditional voting methods while using the convenience and openness of modern technology. Ballot paper or electronic voting machines are the current voting schemes in every nation, and democratic voting is a significant event in every country. Problems with these procedures abound, including lack of openness, poor voter turnout, vote manipulation, mistrust of the election body, forgery of unique identification (voter ID card), delays in disseminating results, and, most importantly, security breaches. Prioritizing the security of digital voting is of utmost importance when contemplating implementing a digital voting system. The article assesses the objective of building a blockchain-based e-voting system [BC-E-VOT] that uses digital voting technology. Electronic voting methods that leverage the distributed ledger attract much attention because they can make digital voting more transparent, secure, and honest. As shown in this research, a successful strategy for electronic voting may be achieved by using Blockchain's cryptographic underpinnings and transparency. Due to its complete transparency, the suggested approach satisfies the essential criteria for electronic voting systems. Since Blockchain employs a decentralized mechanism for data storage rather than storing all of the data in one central place, it becomes challenging to tamper with the data when utilizing this technology to build a decentralized application. By creating a decentralized system using the WSN platform, a third party is no longer needed to oversee the election's access control. This article provides a system for electronic voting that guarantees privacy, trustworthiness, and security. The suggested approach is practical and secure, according to the findings.
This research paper explores the CONIKS key management systemâs security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Open access
Cryptography and Data Security
Security in Wireless Sensor Networks
Advanced Steganography and Watermarking Techniques
Alexandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Kateryna Kuznetsova
Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where âORâ refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.
In wireless sensor networks (WSNs), the presence of malicious nodes (MNs) poses significant challenges to data integrity, network stability, and system reliability. These issues are intensified by energy resource constraints and limitations within centralized authentication systems, necessitating an energy-efficient solution to ensure real-time responsiveness. Although artificial intelligence-driven approaches enhance detection capabilities, they overcome challenges related to data volume, coordination overhead, and latency in centralized control. This study introduces blockchain-machine learning (BC-ML), a novel hybrid model that seamlessly integrates blockchain and machine learning (ML) techniques to effectively identify MNs in WSNs. The model establishes an energy-efficient blockchain among cluster heads (CHs) for robust node authentication, incorporating a Schnorr-like zero-knowledge-proof technique to validate node data during communication initiation. Utilizing a hybrid lightweight approach with both symmetric and asymmetric ciphers enhances the security of node data transmission. A new proof-of-authority method is introduced, which leverages node digital certificates instead of conventional data transactions. This consensus mechanism reduces the processing overhead associated with larger data sizes in traditional proof-of-work methods, thereby improving both energy efficiency and scalability. To address dataset imbalances, the model employs a hybrid unsupervised ML technique, combining adaptive synthetic sampling with a convolutional neural network for efficient analysis of nodes and network features. The ML model, hosted on a robust data server, ensures ongoing oversight by updating CHs with security levels for detected MNs, thereby reducing storage and mitigating coordination challenges. Comprehensive analyses validate the effectiveness of the BC-ML model for detecting MNs, optimizing resource utilization, minimizing delays, and prolonging node and network lifetimes. Security analysis further confirms the ability of the model to mitigate diverse attacks and meet the stringent WSN security requirement.
Wireless Sensor Networks (WSNs) play a crucial role in modern distributed systems, supporting applications such as smart cities, healthcare, and industrial automation. However, their decentralized and resource-constrained nature makes them highly vulnerable to malicious node attacks, including data manipulation, packet dropping, and routing disruption. Traditional detection techniques based on rule-based or statistical methods are inadequate for handling dynamic and complex attack patterns. Recent advancements in Artificial Intelligence (AI), particularly Vision Transformers (ViTs) with cross-attention mechanisms, have significantly improved malicious node detection by capturing global dependencies and contextual relationships in network data. Simultaneously, blockchain technology has emerged as a robust solution for secure, decentralized, and tamper-proof data storage in WSNs. Blockchain-based WSN architectures enhance data integrity, transparency, and trust through distributed ledgers and smart contracts. Studies show that blockchain-integrated detection frameworks can achieve near-perfect classification accuracy while ensuring secure data transmission. Furthermore, hybrid AI-blockchain systems combine intelligent detection with secure storage, improving resilience against attacks. This survey reviews recent methods, compares architectures, identifies research gaps, and highlights future directions for developing secure and scalable WSN systems.
Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment.
Abstract Atomic Cross-Chain Swap (ACCS) is one important topic in cryptocurrency, where users can securely and trustlessly exchange assets between two different blockchains. However, most known ACCS schemes assume specific scripting functionalities of the underlying blockchains, such as Hash Time Locked Contracts (HTLC). In addition, these schemes are typically only applicable to certain digital signature schemes, like Schnorr or Elliptic Curve Digital Signature Algorithm (ECDSA) signatures. In this paper, we propose a generic ACCS scheme, independent from the underlying blockchains. To the best of our knowledge, this is the first solution of this kind. Our results are as follows. First, we define a formal system model of ACCS. Next, we present a generic ACCS scheme meets our model. This scheme admits atomicity in cross-chain swaps without the need for a Trusted Third Party (TTP) and protects usersâ privacy. Finally, by using the Non-Interactive Zero-Knowledge (NIZK) proof protocol as a tool, we instantiate our generic scheme for Elliptic Curve Discrete Logarithm Problem-based (ECDLP-based) signatures. In addition, we implement our scheme, and the experimental results show that our protocol outperforms the existing ACCS schemes, such as the HTLC-based schemes.
When a user wants to access certain services offered by a service provider, typically the user must first authenticate herself with the service provider, such that the service provider may grant authorization to access the services. Authentication is the process through which the user provides confirmation of her identity to the service provider (and, in parallel, the user should receive confirmation that the service provider is legitimate). Several types (or factors) of authenticators can be utilized in this process. Namely, things the user know (e.g.: passwords, PINs); things the user possesses (e.g., token authenticators in the smartphone, key fobs, cards); characteristics or physical traits of the user (e.g., fingerprint, iris pattern); and, as proposed in this dissertation, the userâs location. Each authentication factor has, in terms of security and user experience when compared to other factors, strong and weak aspects, (or pros and cons). For instance, passwords must be long and random, but then remembering them can be taxing; fingerprints are (believed to be) unique and thus form a good authenticator, but they are immutable and hardly confidential; token authenticators and out-of-band tokens (such as SMS tokens) provide an ephemeral value that is valuable for security, but the user might lose possession of the respective token device. Combining two or more of those authenticators results in a potential increased security as compared to utilizing only one authenticator, which is known as multi-factor authentication. â This dissertation focuses on multi-factor authentication. I present a cryptographic method to enable location as an authentication factor, using the flexibility of Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and its access policies, together with location beacons. Such that this location authenticator can be realized, I develop a technique to request and control the presentation of multi-factor authenticators, through which scores are assigned to each authenticator type and both the user and the authentication service are aware of a minimum score needed for full authentication. To address the necessity of a secure scheme through which a user presents the authenticators, I construct a method for conveying the authentication factors in a Zero-Knowledge Password Proof (ZKPP) scheme and through an ephemeral, confidential session. The method also provides a secure joint authenticator that is the cryptographic composite (built within ZKPP) of the individual authenticators. To embody and realize these techniques, I devise a multi-factor authentication protocol named LOCATHE, through which a user device or user can authenticate herself to an authentication service using the deviceâs or userâs location and other authentication factors, with guarantees of forward secrecy. Moreover, I design a Location-Enhanced Multi-Factor Authentication Service (Loc-Auth), abstracting hardware (such as the location beacons) and control into a layered structure, to provide the authentication services and support for the components of this dissertation. Finally, I develop a Proof-of-Concept system, and perform an extensive security evaluation and analysis of the work herein.
In this paper, we present a novel Secure Clustering Routing Method based on Blockchain and Swarm Intelligence (BS-SCRM) for Wireless Sensor Networks (WSNs), which serves as a cornerstone in the Internet of Things (IoT) infrastructure. Recognizing the limitations of existing clustering routing methods in addressing security threats, our approach integrates blockchain technology to fortify WSNs against vulnerabilities such as man-in-the-middle attacks. The proposed BS-SCRM method is structured in two phases: (1) an enhanced cluster head (CH) election utilizing an elite strategy-enhanced Whale Optimization Algorithm (WOA) that considers node energy and proximity to the base station, and (2) a secure data on-chain phase where blockchain comes into play, encrypting and validating cluster data to safeguard integrity and prevent tampering. We further tackle the challenge of implementing blockchain in resource-constrained WSNs by assigning distinct roles to devices, i.e., ordinary nodes with data viewing permissions and accounting nodes entrusted with both data viewing and consensus algorithm execution. Extensive simulations confirm that BS-SCRM not only improves clustering quality but also provides a more secure and energy-efficient routing solution compared to contemporary methods. More specifically, simulation results in different scenarios demonstrate that BS-SCRM enhances network lifetime by 24-73% compared to other clustering methods when facing attacks.
We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only $64$ constraints per multiplication for $2^{15}$ single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has $15.9 \times$ less constraints utilizing single precision floating-point values, and $12.2 \times$ less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing - Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a configuration, Bob can create a proof of (non-)proximity in $0.26 s$, whereas Alice can verify her distance to about $470$ peers per second
Abstract As an important manifestation of the current development and transformation of the worldâs power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.
A mobile ad-hoc network (MANET) necessitates appropriate routing techniques to enable optimal data transfer. The selection of appropriate routing protocols while utilizing the default settings is required to solve the existing problems. To enable effective video streaming in MANETs, this study proposes a novel optimized link state routing (OLSR) protocol that incorporates a deep-learning model. Initially, the input videos are collected from the Kaggle dataset. Then, the black-hole node is detected using a novel twin-attention-based dense convolutional bidirectional gated network (SA_ DCBiGNet) model. Next, the neighboring nodes are analyzed using trust values, and routing is performed using the extended osprey-aided optimized link state routing protocol (EO_OLSRP) technique. Similarly, the extended osprey optimization algorithm (EOOA) selects the optimal feature based on parameters such as node stability and link stability. Finally, blockchain storage is included to improve the security of MANET data using interplanetary file system (IPFS) technology. Additionally, the proposed blockchain system is validated utilizing a consensus technique based on delegated proof-of-stake (DPoS). The proposed method utilizes Python and it is evaluated using data acquired from various mobile simulator models accompanied by the NS3 simulator. The proposed model performs better with a packet-delivery ratio (PDR) of 91.6%, average end delay (AED) of 23.6 s, and throughput of 2110 bytes when compared with the existing methods which have a PDR of 89.1%, AED of 22 s, and throughput of 1780 bytes, respectively.
Internet of Things (IoT) paves the way for the modern smart industrial applications and cities. Trusted Authority acts as a sole control in monitoring and maintaining the communications between the IoT devices and the infrastructure. The communication between the IoT devices happens from one trusted entity of an area to the other by way of generating security certificates. Establishing trust by way of generating security certificates for the IoT devices in a smart city application can be of high cost and expensive. In order to facilitate this, a secure group authentication scheme that creates trust amongst a group of IoT devices owned by several entities has been proposed. The majority of proposed authentication techniques are made for individual device authentication and are also utilized for group authentication; nevertheless, a unique solution for group authentication is the Dickson polynomial based secure group authentication scheme. The secret keys used in our proposed authentication technique are generated using the Dickson polynomial, which enables the group to authenticate without generating an excessive amount of network traffic overhead. IoT devices' group authentication has made use of the Dickson polynomial. Blockchain technology is employed to enable secure, efficient, and fast data transfer among the unique IoT devices of each group deployed at different places. Also, the proposed secure group authentication scheme developed based on Dickson polynomials is resistant to replay, man-in-the-middle, tampering, side channel and signature forgeries, impersonation, and ephemeral key secret leakage attacks. In order to accomplish this, we have implemented a hardware-based physically unclonable function. Implementation has been carried using python language and deployed and tested on Blockchain using Ethereum Goerli's Testnet framework. Performance analysis has been carried out by choosing various benchmarks and found that the proposed framework outperforms its counterparts through various metrics. Different parameters are also utilized to assess the performance of the proposed blockchain framework and shows that it has better performance in terms of computation, communication, storage and latency.
Slavica TomoviÄ, Bogdan KrivokapiÄ, Äula NaÄ, Igor RadusinoviÄ
This paper introduces a new blockchain-assisted key management protocol specifically designed for clustered Underwater Acoustic Sensor Networks (UASNs). The protocol focuses on simplifying cross-cluster reauthentication for mobile underwater devices and reducing the risks of internal attacks caused by compromised nodes. To address the resource limitations of UASN devices, the protocol utilizes Elliptic Curve Qu Vanstone (ECQV) certificates, which considerably reduces Public Key Infrastructure (PKI) overhead. Moreover, it integrates the Hashed One-pass Menezes-QuVanstone (HOMQV) protocol with blockchain technology to enhance key exchange security against active attacks. The blockchain is hosted on resource-intensive surface nodes that serve as cluster heads and decentralized authorities for certificate management. Smart contracts embedded within the blockchain facilitate certificate-related operations and node trust scoring mechanism. We compared the proposed solution against a state-of-the-art benchmark and demonstrated that it imposes lower computational and communication overheads while exhibiting robustness against various attacks, as confirmed by the AVISPA tool. The solution is implemented on actual underwater/surface devices and validated experimentally. By leveraging the immutable, traceable, and fault-tolerant properties of blockchain, it establishes a secure, scalable, and efficient communication framework for mission-critical UASN applications.
We present a passwordâauthenticated (2, 3)âthreshold group key share (PATS) mechanism. Although PATS resembles threshold secret sharing schemes, it has a different structure. The innovative perspective of the PATS mechanism that makes a difference from the standard secretâsharing schemes is that it involves parties in the generation of the shares. PATS allows parties to communicate securely to establish their shares over insecure channels. Parties (shareholders) construct a secret (key) using shares obtained at the end of the protocol. PATS takes advantage of zeroâknowledge proofs compared to wellâknown threshold key exchange schemes and will tolerate the existence of semiâtrusted parties. We present two variants of PATS, centralized and distributed, and then generalize PATS to ( t , n )âthreshold scheme. PATS supports the distributed operation and optionally facilitates group key verification by a trusted third party, which may also partake in group key sharing. In this paper, we present PATS, which employs finite fields and elliptic curves, along with its security and complexity analyses.
Wireless Sensor Networks (WSNs) are essential for data collection across various domains but face growing risks from replication attacks, which introduce new vulnerabilities and security challenges. To address this issue, we propose a novel hybrid approach that integrates Distributed Ledger Technology (DLT) with adaptive Machine Learning (ML) methods, aiming to bolster both security and trustworthiness within WSNs. Specifically, our approach utilizes DLT to secure voting records and manage rewards, while adaptive ML models detect replica nodes by analyzing network parameters, including location, signal strength, and transmission rate. We present and evaluate three ML-based models for detecting replication attacks: 1) Random Forest Model (RFM), 2) Adaptive Weighted Random Forest Model based on Predicted Replica Nodes (AWRFM-PRN), and 3) Adaptive Weighted Random Forest Model based on Predicted Good and Replica Nodes (AWRFM-PGRN). The AWRFM-PRN and AWRFM-PGRN models enhance detection accuracy through iterative weight adjustments based on previous predictions. Our simulations show that the hybrid approach significantly improves detection performance compared to traditional methods. We evaluated our models by increasing the dataset size with varying proportions of replica nodes across ten subsets. We found that the AWRFM-PGRN model achieved around 71% accuracy when replica nodes comprised 50% or more of the network. Meanwhile, the AWRFM-PRN model demonstrated high effectiveness with accuracy ranging from 80% to 99% for replica nodes constituting 15% to 40% of the network. Furthermore, all models delivered nearly 99.9% accuracy when the proportion of replica nodes was between 5% and 10%. This innovative integration of DLT with adaptive ML modeling establishes a benchmark for robust and tamper-proof security in WSNs, offering significant enhancements over traditional ML techniques such as RFM, particularly in scenarios with high replica node counts.
The rapid advancement of grid modernization and the proliferation of smart grids have engendered a critical need for cyber-physical security. Recent cyber-attacks targeting grid infrastructure, notably leading to substantial blackouts in Ukraine, underscore the vulnerabilities and potentially catastrophic consequences of such incursions. These attacks, whether stemming from cyber threats such as Denial of Service (DOS), False Data Injection Attacks (FDIA), or complex cyber-physical manipulations, emphasize the imperative of robust cybersecurity protocols in smart grid operations. This research investigates a pivotal approach to fortify and safeguard smart grid systems by integrating blockchain technology with wireless sensor nodes. By leveraging a Proof of Authority (PoA) Ethereum Blockchain framework, the study delves into the transformative capabilities of Blockchain within Supervisory Control and Data Acquisition (SCADA) networks. Specifically, it examines configurations across IEEE 14-bus, 30-bus, and 118-bus topologies. In addition to elucidating the inherent vulnerabilities in traditional SCADA systems, this study meticulously evaluates an array of performance matrices. Statistical analyses encompassing mean, standard deviation, skewness, kurtosis, and confidence levels provide nuanced insights into the efficacy of blockchain mechanisms in enhancing SCADA resilience against contemporary cyber threats. This research endeavors to bridge the gap in modern cybersecurity paradigms by fusing blockchain technology with wireless sensor nodes. By fortifying data integrity, elevating the reliability of data transmission, and augmenting trustworthiness within SCADA infrastructures, this study aims to present robust solutions to the escalating cybersecurity challenges faced by smart grid systems.
Liqun Chen, Changyu Dong, Christopher J. P. Newton, Yalan Wang
Group signatures and their variants have been widely used in privacy-sensitive scenarios such as anonymous authentication and attestation. In this paper, we present a new post-quantum group signature scheme from symmetric primitives. Using only symmetric primitives makes the scheme less prone to unknown attacks than basing the design on newly proposed hard problems whose security is less well-understood. However, symmetric primitives do not have rich algebraic properties, and this makes it extremely challenging to design a group signature scheme on top of them. It is even more challenging if we want a group signature scheme suitable for real-world applications, one that can support large groups and require few trust assumptions. Our scheme is based on MPC-in-the-head non-interactive zero-knowledge proofs, and we specifically design a novel hash-based group credential scheme, which is rooted in the SPHINCS+ signature scheme but with various modifications to make it MPC (multi-party computation) friendly. The security of the scheme has been proved under the fully dynamic group signature model. We provide an implementation of the scheme and demonstrate the feasibility of handling a group size as large as 2 60 . This is the first group signature scheme from symmetric primitives that supports such a large group size and meets all the security requirements.
In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention.
Carsten Baum, Samuel Dittmer, Peter Schöll, Xiao Wang
Abstract A zero-knowledge proof is a cryptographic protocol where a prover can convince a verifier that a statement is true, without revealing any further information except for the truth of the statement. This article is a survey of recent developments in building practical zero-knowledge proof systems using vector oblivious linear evaluation (VOLE), a tool from secure two-party computation. In this work, we attempt to systematize the recent works on VOLE-based Zero-Knowledge proofs and make the state of the art accessible in one document.
Blockchain has proven in sensor networks as a distributed solution for transparent and secure storage, which allows its application in mobile wireless sensor networks (MWSNs). The consensus mechanism, an essential aspect of blockchain technology, must concern the high mobility, resource-constrained nature, and weak physical defenses of sensor nodes in MWSNs. To secure MWSN data storage in clustered communication, we design a proof-of-information (PoI) variant for fair miner campaigning via the amount of valid data generated from environmental information, including a dynamic adjustment of the data volume threshold to detect malicious nodes and prevent them from misreporting information. Additionally, we introduce a filtering mechanism through the dynamic integrated trust (DIt) of nodes, which integrates the trust evaluation of peer nodes across the network combining objective performance to prevent malicious nodes from infiltrating the final consensus group. The multi-level filtering technique improves the campaign fairness while isolating malicious nodes, ensuring complexity-sensitive PBFT algorithm efficiency in large-scale networks. Simulation results show that the scheme isolates 90% of the malicious nodes and screens 20% of members to produce a smaller final consensus group. Further analysis of impacts on the performance considering network topology and mobility patterns and comparisons of the relevant solutions are presented.
T. Gobinath, Sanjay Kumar Sonkar, Vinod N. Alone, C. Thiripurasundari
As a distributed and decentralized ledger that ensures secure and transparent transactions, blockchain technology has attracted considerable interest. In the context of wireless sensor networks (WSNs), where nodes with limited resources conduct transactions, ensuring efficient and trustworthy validation becomes a challenge. Using random forests, this paper proposes a novel method for enhancing blockchain transaction validation in WSNs. The proposed method enhances the accuracy and efficiency of transaction validation in WSNs by leveraging the ensemble-learning capabilities of random forests. The random forests model is trained with transaction content, originating node information, and network metrics extracted from WSN transactions. Experimental results indicate that the proposed method improves transaction validation precision and decreases validation time in comparison to conventional methods. In addition, the random forests model is resistant to multiple types of attacks, assuring the security and integrity of WSN transactions. The results demonstrate that random forests are a promising technique for improving blockchain transaction validation in wireless sensor networks.